This paper introduces DynamicRenderNet, a novel neural network architecture designed to optimize graphics rendering in real-time Augmented Reality (AR) and Virtual Reality (VR) games. By adaptively adjusting rendering parameters based on scene complexity and user interaction, DynamicRenderNet significantly improves frame rates and reduces latency. Our model utilizes a deep learning approach to analyze real-time input data, such as camera feed, user head movements, and scene geometry, to predict optimal rendering settings. This enables dynamic resource allocation and prioritization, leading to a more efficient and responsive rendering pipeline. Experimental results demonstrate substantial improvements in frame rates and reduced latency, resulting in a more immersive and visually appealing AR/VR experience.

Risk analysis is a key process in project management, aiming to evaluate and mitigate risks by quantifying the probability and severity of potential events. A common tool for this purpose is the risk matrix, which offers a structured visualization of risks. Despite its effectiveness, constructing risk matrices traditionally depends on domain experts to define event probabilities and severities, leading to scalability and consistency challenges. This paper introduces RMGNN (Risk Matrix Generation with Graph Neural Networks), a semi-supervised and transductive method for automating risk matrix construction. RMGNN models historical events as graph structures, leveraging graph neural networks and language models to estimate event probabilities and severities. The method integrates labeled and unlabeled data, reducing reliance on manual annotation while maintaining accuracy. The main contributions of this paper include: (1) a graph-based framework for semi-supervised and transductive risk matrix learning, (2) a unified approach for estimating event probability and severity from graph representations, and (3) the use of graph topology to enhance interpretability in risk assessment. Experiments on real-world datasets demonstrate the effectiveness of RMGNN in addressing challenges associated with traditional risk matrix construction, supporting improved risk analysis and decision-making processes.

This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternative parameter defined as the distance between the origin and the separatrix in the bit-cell state space to quantify the mismatch of the cell. The resulting distribution of this parameter obtained from Monte Carlo simulations is then related to the start-up probability distribution using a two-component logistic function. The reported results show that the proposed imbalance factor is a good predictor for PUF-related reliability estimation with the advantage that can be applied at the early design stages.

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment during the power-up process, they are subject to low challenge-response repeatability. The dependability of SRAM-PUFs is usually accomplished by combining complex error correcting codes (ECCs) with fuzzy extractor structures resulting in an increase in power consumption, area, cost, and design complexity. In this study, we established effective metrics on the basis of the separatrix concept and cell mismatch to estimate the percentage of cells that, due to the effect of variability, will tend to the same initial state during power-up. The effects of noise and temperature in cell start-up processes were used to validate the proposed metrics. The presented metrics may be applied at the SRAM-PUF design phases to investigate the impact of different design parameters on the percentage of reliable cells for PUF applications.

The present study investigates the stability and in vitro bioavailability of an Acyclovir-loaded Ophthalmic Lyophilisate Carrier System (OLCS) designed for enhanced ocular drug delivery. Acyclovir, an antiviral drug commonly used for treating herpes simplex keratitis, faces challenges such as low bioavailability and limited corneal permeability in conventional formulations. The OLCS approach leverages lyophilized carriers to provide improved stability, controlled drug release, and targeted delivery to ocular tissues. The research evaluated the long-term stability of OLCS under different environmental conditions (accelerated and real-time) in terms of drug content, physical integrity, and reconstitution properties. Stability was assessed using analytical techniques such as high-performance liquid chromatography (HPLC) and differential scanning calorimetry (DSC). The in vitro bioavailability was studied through corneal permeation assays using excised corneal tissues, with comparison to standard acyclovir eye drops. Results showed that OLCS exhibited excellent stability over 12 months, with negligible drug degradation and preserved lyophilized structure. Reconstituted formulations displayed consistent physicochemical properties, including pH and osmolarity. In vitro bioavailability testing demonstrated significantly higher drug permeation and retention in corneal tissues compared to conventional formulations, attributed to the enhanced mucoadhesive and controlled release properties of the OLCS. The findings suggest that Acyclovir-loaded OLCS is a promising alternative to conventional ocular delivery systems, offering improved therapeutic performance and long-term efficacy. Further in vivo studies are warranted to validate its clinical applicability for managing ocular viral infections.

The United States housing market has historically exhibited regional imbalances in housing supply and demand, which have contributed to reduced housing affordability and market volatility. The application of big data technology enables the utilisation of data to enhance comprehension of these imbalances, thereby informing the formulation of policy. We put forth a model for analyzing the housing supply and demand based on big data, which employs a comprehensive approach to examine both the demand and supply sides. With regard to the demand side, the model incorporates a multitude of data sources to ascertain and delineate the pivotal elements influencing housing demand. These include population growth rate, household income level, employment opportunity distribution, migration and flow trends, and cost of living. By constructing cubes, the model is capable of capturing the characteristics of dynamic demand changes in different regions. With regard to the supply side, the model assesses land use, building materials and labor costs, the timeliness of building permitting and approval processes, and the impact of regional policies and regulations. By means of a quantitative analysis of the aforementioned factors, the model is able to identify housing supply bottlenecks in different regions. The model's efficacy in identifying significant imbalances between supply and demand in the United States housing market was validated through experimental analysis of historical data.

The objective of this study was to develop and evaluate an acyclovir-loaded ophthalmic lyophilisate carrier system, aimed at enhancing ocular drug delivery for the treatment of viral eye infections. Acyclovir, an antiviral agent, is often prescribed for ocular herpes simplex virus infections, but its ocular bioavailability is limited due to rapid drainage and poor permeability. To address these limitations, a novel lyophilized formulation was designed using biocompatible and biodegradable polymers as carriers to prolong the retention time and facilitate sustained release of the drug at the site of infection. The acyclovir-loaded lyophilisates were prepared using a freeze-drying technique, with optimization of the formulation parameters including polymer concentration, cryoprotectants, and lyophilization conditions. The characterization of the lyophilized systems included evaluation of physical properties, drug content, reconstitution behavior, morphology, and in vitro release profiles. The formulations exhibited high drug entrapment efficiency, desirable rehydration properties, and sustained drug release over an extended period. In vivo ocular tolerability of the developed system was assessed in a rabbit model. The formulations showed good ocular compatibility with minimal irritation or inflammation, indicating their potential for safe and effective use in ocular therapy. The pharmacokinetic study revealed enhanced corneal retention and prolonged drug release compared to conventional aqueous solutions. This study demonstrates the potential of acyclovir-loaded ophthalmic lyophilisate carrier systems for improved ocular drug delivery. The developed formulation provides a promising alternative to conventional therapies, offering prolonged ocular residence time, improved drug bioavailability, and minimal side effects, thereby enhancing the therapeutic efficacy of acyclovir in treating viral ocular infections. Further clinical studies are warranted to confirm these findings and explore the potential for broader clinical application.

This paper studies the capacity limits of multi-input multiple-output (MIMO)-reconfigurable intelligent surface (RIS) communication systems when the number of transmit antennas, Nt, receive antennas, Nr, or RIS elements, M , is asymptotically large. We derive a novel asymptotic approximation and upper bound of the MIMO-RIS capacity formulated in a simple enough manner to provide meaningful insights that can then be translated into practical optimization guidelines. Our study reveals that the MIMO-RIS capacity can be decomposed into a line-of-sight (LOS) and a non-LOS (NLOS) part in the large asymptotic Nt, Nr, or M regime. We then also prove that the LOS and NLOS parts of the capacity can further be decomposed into the product between an array gain, i.e., related to the RIS amplitude response, and a directionality gain, i.e., related to the RIS phase response, when M is large. The array gain tends to be larger than the directionality gain, and the latter vanishes in certain conditions. These insights have clear implications for RIS optimization and lead to some simple practical guidelines. For instance, in the large Nt or Nr regime (in the presence of intra-element coupling at the RIS) as well as the large M regime (when √ NtNr is sufficiently large compared to M), the capacity of MIMO-RIS can be optimized without the need for complex channel state information (CSI) estimation since it mainly depends on the RIS amplitude response. As such, it can be optimized by using a simple fixed phase allocation. Whereas in the large Nt or Nr regime and in the absence of intra-element coupling at the RIS, the best strategy for improving the MIMO-RIS capacity is to optimize the RIS phase solely based on the LOS path. This can be easily done via any classic MIMO beamforming method.

To detect contaminants accidentally included in packaged foods, food industries use an array of systems ranging from metal detectors to X-ray imagers. Low density plastic or glass contaminants, however, are not easily detected with standard methods. If the dielectric contrast between the packaged food and these contaminants in the microwave spectrum is sensible, Microwave Sensing (MWS) can be used as a contactless detection method, which is particularly useful when the food is already packaged. In this paper we propose using MWS combined with Machine Learning (ML). In particular, we report on experiments we did with packaged cocoa-hazelnut spread and show the accuracy of our approach. We also present an FPGA acceleration that runs the ML processing in real-time so as to keep up with the throughput of a production line.

With rapid digitization and digitalization, drawing a fine line between the digital and the physical world has become nearly impossible. It has become essential more than ever to integrate all spheres of life into a single Digital Thread to address pressing challenges of modern society – accessible and inclusive healthcare in terms of equality, and equity. The techno-social advancements and mutual acceptance have enabled the infusion of digital models to simulate social settings with minimum resource utilization to make effective decisions. However, there is a significant gap in feeding back the models with appropriate changes in real-time. In other words, an active behavioral modeling of modern society is lacking, that influences community healthcare as a “whole”. By creating virtual replicas of physical systems, digital twins can enable real-time monitoring, simulation, and optimization of urban dynamics. This paper explores the potential of digital twins to promote inclusive healthcare for evolving smart cities. We argue that digital twins can be used to:Identify and address disparities in access to healthcare servicesFacilitate community participationSimulate the impact of urban policies and interventions on different groups of peopleAid policy making bodies for better access to healthcareThis paper proposes several proposals for stitching the actual and virtual societies using digital twins. Several discussed concepts within this framework envision an active, integrated, and synchronized community aware of data privacy and security. The proposal also provides high- level step-wise transitions that will enable this transformation.